A few years ago, ML algorithms looked strange and difficult for an average software engineer. ML is growing really fast. Nowadays it is easy to improve production solution by some Artificial Intelligence. You don’t need to have twenty people in your Data Scientist department if you want to extend you service with smart analytics or Artificial Intelligence.
I will show you how to apply smart search in your service.
Currently, our service is a place, where each user can share their articles, documents, videos, calendar events, tasks and etc. So we have a huge database with users’ content. Now it is a problem for a user to search a certain document or event. All items have tags and full text search. But what about video and audio files?

Once the tensor operations are desugared, a transformation we call “partitioning” extracts the graph operations from the program and builds a new SIL function to represent the tensor code. In addition to removing the tensor operations from the host code, new calls are injected that call into our new runtime library to start up TensorFlow, rendezvous to collect any results, and send/receive values between the host and the tensor program as it runs. The bulk of the Graph Program Extraction transformation itself lives in TFPartition.cpp.

Once the tensor function is formed, it has some transformations applied to it, and is eventually emitted to a TensorFlow graph using the code in TFLowerGraph.cpp. After the TensorFlow graph is formed, we serialize it to a protobuf and encode the bits directly into the executable, making it easy to load at program runtime.